Based on these characteristics, we dubbed this kind of teamwork FLECS, because the team has a flat structure (no one is in charge); the team's activities are loosely coupled, have extended duration, and are continually being revised; and team members typically act in a syncopated manner, not synchronously.

team members have very little of the detailed knowledge of each other's plans required by value-of-information approaches to determining what information to share.


In order to compute the derivational entropy as described in Section 4, it is necessary to guarantee that the automaton is consistent.

the weights of the edges in the WFA have to be normalized, because the normalization is a sufficient condition for the WFA to be consistent and to become a PFA if all states appear at least in one path.


Then, for a given path θ in A of size k: The last equation is because N (0) is the weight accumulated in all paths in the WFA, W A (Θ A (0, |Q| − 1, •)) (as we will see below), and W A (θ) is defined in a way similar to Equation (6).

the normalization keeps the relative weight distribution of the paths in the normalized PFA A .


Note that Each M k (i, j) is in fact the addition of the weights of all paths from i to j (i, j = |Q| − 1) with length k that have at most k + 1 different states.

if i = j, i = |Q| − 1, expression (11) becomes: given that there are no cycles, only loops.


Case iii in Equation (18) for a given i and v (0 < i ≤ |Q| − 2, v ∈ Σ) corresponds to the addition of all paths that can be seen in Figure 6.

equation (18) for these loops becomes: The value ( * ) represents the product of all strings that can be composed by traversing the loops in state i (1/(1 − r i )) multiplied by the probability of transition (i, v, i) multiplied again by the product of all strings that can be composed traversing the loops in state i.


If f is total, then every label except l 0 is fused into exactly one hole, hence, has exactly one parent in T U, f .

with U being finite, if we start at any arbitrary node and follow the sequence of parents, we have to end up at l 0 .


Finally, a partially scoped UG U, even if all the ETs of U are standard, will inevitably have these non-standard tree structures.

for the sake of the robustness of our definitions, we should be able to model these structures.


Accordingly, detach the treeτ n fromT , replace it withτ b n and call the new treeT , as demonstrated in Figure 8(b).T is a solution ofÛ , and hence (based on the induction assumption) is a solution of U .

all qeq constraints in U are satisfied inT , which also proves: All qeq constraints in U are satisfied inT .


In this article, we shall use the notion of semantic dependency graph to compare our framework with Hole Semantics (Section 7.3).

in the rest of this section, we formally define this notion and prove that the two formulations of coherence are in fact equivalent.


As a result, there is no need to distinguish floating scopal (i.e., quantifier) ETs; and hence, there is also no L q component, which in turn means that there is no designated top ET in dominance graphs.

all labels can potentially form the root of a solution.


Based on condition (T7c), G−E has at least m WCCs.


2004) and also to compute G−E for some free ET E, where |G| = def |L| + |H| + |E| + |D|.

sAT (as well as each branch of ENUM) runs in O(|L||G|) time, meaning that the worst-case time-complexity of sAT (and each branch of ENUM) is quadratic in the size of G. This result is for weakly normal hypernets-that is, when there are no label-tohole constraints.


Given any two arbitrary nodes u and v belonging to trees T i and T j , both i and j can reach the heart through some directed path in SDG U .

l q i and l q j are hyper-normally connected to the heart through hyper-normal paths p 1 and p 2 , as shown in Figure 16(b).


Because U is a CF-UG, E must be a floating scopal ET.


One of the two parts, say the segment of P between h 4 and l 2 , is not directed.

p has a segment like the one in the figure on the right.


For |P| = 1, j immediately dominates i in SDG U , hence there is a dominance edge from l q j to some node l r i ∈ T r i , where T r i is the restriction tree of l q i (Definition 13).

l r i must be in the scope of l q j in T .


According to the induction assumption, l q k , and hence l r i , is in τ r i .


Thus, because bags of T correspond to the cache at each step of the parser, the parser's cache will contain u at the step at which v is pushed into the rightmost position of the cache.

the automaton can build each edge of G. Combining Lemmas 3 and 4, and using Lemma 1 from Section 2, we have the following main result, which is a characterization of the relative treewidth of a graph with respect to an ordering of its vertices.


Let i 1 ∈ I be the node of T produced by this push transition, and let T 1 be the subtree of T rooted at i 1 .

the vertices of G that are pushed into the cache in the run associated with t 1 cannot contain any neighbor of v. v is not needed in t 1 .


Again, tree T is a smooth tree decomposition of G, with the same width as T. Furthermore, the replacement of v 2 by v 1 does not affect the bags of T where these nodes have been introduced for the first time.


If there are h such distinct endpoints, B must contain these h vertices and the vertex introduced at B, for a total size of h + 1 vertices.

the width of T is at least h. The combination of Lemmas 6 and 7 leads to an efficient algorithm for finding the relative treewidth of G with respect to π, reported below.


Suppose that D ∈ [[M]] + and let ρ be an accepting run of M on D. is still acyclic (because e 1 and e 2 belong to distinct connected components of D) and ρ is an accepting run on D[e 1 ↔ e 2 ] as well.

now, let us turn M into M by adding a unique leaf symbol , adding a new state f and the transition {f } − → ∅, and turning all original transitions of the form the DAGs recognized by M are those originally recognized by M, but with additional leaves labeled added as leaves below the original leaves, and the accepting runs of M are those of M, extended by labeling the (unique) outgoing edges of the original leaves with f .


Courcelle (1990, Corollary 4.8) shows that the restriction of an HR language by a property expressible in MSO logic yields an HR language (for which a suitable HRG can effectively be constructed).

it suffices to argue that every recognizable DAG language is definable by an MSO formula.


Thus, [[M]] = ∅ if 0 is reachable from itself in N. In a similar way, if M accepts a DAG D, a run of M on D can easily be turned into a nonempty firing sequence of N (under the top-down interpretation of runs) that turns 0 into itself.

we have reduced the emptiness problem for DAG automata to the problem of deciding whether a Petri net is structurally cyclic.


For example, in Figure 17, these are the hyperedges [x, ], [y, ], and [u, ], which are covered by {e(x, 1), e(x, 2), e(y, 1), e(y, 2), e(u, 1), e(u, 2)}.

assume finally that b is not the root of T. If cont T (b) = {v 1 , .


Note that the unions I 1 I 2 and O 1 O 2 in item 2 (and similarly in item 4) are multiset unions.

no states are "lost" if I 1 and I 2 or O 1 and O 2 overlap.


, q n }, because the edges in in D (v) and out D (v) are those whose targets and sources, respectively, are the leaves of T v in D T .

the restriction of ρ to E D is a run of M on D. This argument yields the following theorem.


This leaves 2(n − 1) internal edges in each treelet ), two edges internal to T u , and two edges internal to T v .

we are now left with the comparison of the two terms c .


Thus, from this point onwards up until the start of Section 7.4, all DAG automata and extended DAG automata are assumed to be unweighted.

the m-automata A = (Ξ, Q, τ, s, ρ) appearing in this section are also unweighted.


Then M contains the transitions − − → s (this assigns the initial state of A to the top-most edge of the spine), − − → {ξ } for all ξ ∈ Ξ, q ∈ Q, and ξ, q, ξ ∈ τ (this "reads" q on an incoming edge in state ξ, assigning the resulting state ξ to the next edge on the spine), − − → {s } for all final states ξ of A (this allows A to "cross" the middle of the spine and continue to work on the outgoing edges) and, similarly to the preceding items, − − → {ξ , q} for all ξ ∈ Ξ , q ∈ Q, and ξ, q, ξ ∈ τ (similarly to the second item, but "reading" q on an outgoing edge) and .

we can extend three results from non-extended DAG automata to extended ones: The emptiness and finiteness problems are decidable and the path languages are regular.


This amounts to saying that a string w ∈ Σ * is a path in D if and only if there is a path w in D that satisfies the following: w has a prefix of the form σ 0,1 σ 1,1 , (b) w has a suffix of the form σ 1,1 σ 1,0 , and (c) w is obtained from w by applying the homomorphism that replaces each σ 1,1 by σ and erases all other symbols.

because the path language of [[M ]] is regular, so is that of [[M]], because it is obtained from the former by intersection with two regular languages and applying a homomorphism.


The unary rule does nothing, simply skipping the node, and the binary rule concatenates two sub-computations in A σ : Finally, we need transitions to process leaf nodes at the treelets in D , where we need to simulate transitions of A σ that process edges copied from the original DAG D. Recall that these copied edges are labeled with states in Q, whereas in A σ states from Q are indicative of outgoing edges.

we use the following transitions: The following transitions handle the special case of a treelet consisting of a single node: Once M has been constructed from M, we can process each input DAG D by converting it into a binary DAG D and then by running M on D using Algorithm 2.


Thus, we use the following transitions: The following transitions handle the special case of a treelet consisting of a single node: Once M has been constructed from M, we can process each input DAG D by converting it into a binary DAG D and then by running M on D using Algorithm 2.

as with the algorithm of Section 6 for non-extended DAGs, the running time is exponential in the treewidth of the input DAG, linear in the total size of the input DAG, and, for a fixed treewidth, polynomial in the number of states of the extended automaton and in the number of states of the largest m-automaton A σ for the transitions on a single input symbol σ.


The algorithm maintains a chart with entries chart b [φ] ∈ K, for each b and for each φ ∈ Φ(b).

if m is again the size of the largest Ξ σ , chart b has at most m 2|cont(b)| entries and could be thought of as an order-2|cont(b)| tensor.


A discourse unit is induced by either one multinuclear relation, or one or more mononuclear relations with the same nucleus; the subconstituents of a discourse unit are the members of its inducing relations.

the discourse structure of a text in RST is a constituent tree of discourse units linked by relations.


(2013) show that differences between frameworks can be encoded with different, additional structural constraints on a dependency based graph.

dependency graphs capture commonalities between these different types of representation, and it is easy to convert them into a dependency graph between EDUs.


The point is, the nuclearity measure by itself is not telling us much about the structure of the tree that is relevant to the semantics.

we need a heuristic to go from nuclearity decorations on spans to head EDUs.


Laparra and Rigau's (2013) work on implicit semantic role labeling assumes that different occurrences of the same predicate in a document likely maintain the same argument fillers.

we can identify the antecedent of a bridging anaphor a by analyzing the nominal modifiers in other NPs with the same head word as a.


In addition, we observe that diverse bridging relations in ISNotes, especially many context specific relations such as pachinkodevotees or palmsthe thieves, lead to few training instances for each type of relation.

generalizing is difficult for the learning-based approach.


In DA foc Constraint (33) is not imposed.

whereas strong focalization imposes maximal alternating phases of reversible and irreversible rule application, weak focalization does not impose this maximality.


in the same way, we have that in case (37b), we have the following proof in DA foc : As before, we apply Cut-elimination to the above proof.


Unlike TOEFL11, the ASK corpus is not designed for NLI.

we cannot expect that the essays will be balanced for topic or proficiency, nor for length.


However, it is worth remembering that for these applications, we are primarily interested in the structural descriptions that the grammar assigns to a generated sentence, not in the membership of the sentence per se.

the universal recognition problem is a more accurate model of parsing than the membership problem, as the latter also admits decision procedures where the grammar is replaced with some other mechanism that may produce no or completely different descriptions than the ones we are interested in.


To implement the second part of the derivation, for each clause c i we introduce a lexicon entry c i : Our rules make crucial use of variable restrictions.

to introduce them we define the following notational shorthands: 1 j is a sequence of n slash-variable pairs, except that the jth variable has been replaced with the concrete (atomic) category [v j → 1], and similarly for 0 j .


It is not hard to see that, as long as we work under the restriction to polynomial space, every move to the left in the standard model can be simulated by a (polynomial) number of moves to the right in the circular tape model.

even ATMs with polynomially bounded circular tape precisely characterize EXPTIME.


In the grammar G, this corresponds to the fact that there exists an applicable rule that leads to acceptance.

we do not need to explicitly simulate all possible transitions.


At this point, only some rule of type (26) can be applied, resulting in the reduction of the stack, immediately followed by some rule of type (25), which is a composition rule of degree m. If the derivation were to use more than one occurrence of (26), then it would derive a category whose stack contains fewer than m elements.

rule (25) would no longer be applicable, because of the restriction on the composition degree, and the whole derivation would come to a dead end.


Within the data set, we only have binary labels for true and false MWEs.

any ordering of the MWEs within the block of MWEs labeled as true, respectively, false, does not change the upper bound.


The evaluation on Wikipedia is slightly different, as we do not have any gold data.

we compute the ranking regarding the multiwordness for all words in the corpus.


However, for all other measures, the performance is similar to these baselines or even inferior, and significantly worse than using DRUID with JoBimText.

we will not report results for the other MWE extraction experiments.


Next, we apply our method to candidates without any POS filtering and report results for candidates surpassing a frequency threshold of 10.

we do not only restrict the evaluation on noun MWEs but use all MWEs of all POS classes that have been annotated in both corpora.


However, only for few terms do we observe nested candidates in the most similar words.

we require methods to generate "back-off" candidates.


For Dutch, no trained models for JWS and ASV are available.

we did not use these tools but compare to the NL splitter, achieving a competitive precision but lower recall.


Building a parallel corpus, however, is expensive and time-consuming.

parallel data are limited or even unavailable for many language pairs.


One of the key challenges with the proposed latent variable log-linear model is the high computational complexity of training, as it requires normalizing globally via summing over all possible observations and latent variables.

an exact implementation is impractical even for the moderate vocabulary size of most lowresource languages.


Although there are O(V 2 ) such possible features, we only include the ones that are observed during sampling.

our feature weights vector w is sparse, with most of the entries equal to zero.


However, our results required no more than 1,000 lines of data from each language, and preserved accuracy with as little as 100 lines of data.

this method in its current form is most applicable to languages with extremely limited available data.


6 The phenomenon we discuss in this article has been a subject of interest for linguists, philosophers, and computational linguists for decades.

it has been addressed in various contexts from a variety of perspectives, as discussed in the following sections.


What makes the resolution of non-NA anaphora particularly challenging with respect to conventional anaphora resolution or coreference resolution is that precisely those features that make those tasks tractable problems are the ones that are missing in this domain.

systems that attempt the resolution of non-NA anaphora must attempt to access semantic or discourse-related information, which is not always easily accessible, in order to make resolution decisions.


The function will be successful on the proxy DE Engine 1 goes to Avon to get the oranges in (43a), as the verb goes is a tensed eventive verb.

the referring function states that that refers to the event of Engine 1 getting to Avon, which takes two hours.


As a result of the second of these reports, which determined that AI was never going to work, PhDs in artificial intelligence like my classmate Geoff Hinton and myself spent ten years or so after graduation in psychology departments (in my case, at the Universities of Sussex and Warwick), until yet another report said AI was working after all and that Britain and the U.S. were falling behind Japan in this vital area.

i could get hired again in computer science, first briefly back at Edinburgh, and then at the University of Pennsylvania (i learned a lesson from this odyssey that i have tried to remember whenever i have been appointed to a committee to report on anything, which is that while reports very rarely do any good, they can very easily do a great deal of harm.)


As we saw earlier, in some Germanic languages like Dutch, Swiss German, and West-Flemish, serial verbs are linearized using such rules to require crossing discontinuous dependencies.

b 2 rules give CCG slightly greater than context-free power.


Only humans seem to be able to do the latter.

chimpanzees solve the (mistitled) monkey and bananas problem, using tools like old crates to gain altitude in order to obtain objects that are otherwise out of reach (Köhler 1925) (Figure 1(a)).


Though information extraction (IE) research has more than a 25-year history, F 1 scores remain low.

one could question continued investment in IE research.


Though co-occurrence of words/phrases/topics/names in messages can suggest that a relation exists between them, the type of relation is lacking.

though the following statistics are estimates only, the magnitude of public human language communication is staggering: • 3 million news articles/day 5 • Over 3 million blog posts/day 6 • 500 million tweets/day 7 learning the types of relations, the types of events, and so forth.


Manual updates had been introduced quarterly, and tend to lag significantly behind real time.

the second case study is automatic discovery, characterization, and tracking of actors to automatically suggest updates through information extraction.


Though high F 1 favors recall and precision being similar, some applications can take advantage of IE systems tuned for higher precision (at the expense of recall), or higher recall (at the expense of precision).

though the scientific community can benefit by benchmarking performance based on F 1 , F 1 is not necessarily predictive of fieldability.


This interactional or intersubjective aspect of stance has also been the focus of several others (Precht 2003;White 2003;Kärkkäinen 2006;Keisanen 2007).

the concept of stancetaking provides a unified framework for analyzing the different forms of interactional styles.


3 Though Ritter, Cherry, and Dolan (2010) separate discourse, topic, and other words for modeling conversations, their model focuses on dialogue act modeling and only yields one distribution for topical content.

their model is unable to distinguish varying latent topics reflecting message content underlying the corpus.


Previous research has demonstrated that standard topic models, essentially focusing on document-level word co-occurrences, are not suitable for short and informal microblog messages because of the severe data sparsity exhibited in short texts (Wang and McCallum 2006;Hong and Davison 2010).

one line of previous work focuses on enriching and exploiting contextual information.


For example, in Table 4, the topic generated by the TOPIC+DISC+REL model contains sentiment words like "wrong" and "hate."

a direct use of the topic representations to extract summaries will unavoidably select messages that mostly reflect sentimental component, which is also illustrated by the case study in Section 5.3.


Error analysis on the produced summaries has shown that sentiment should be effectively captured and separated to further advance our current summarization system forward.

the joint effects of discourse, topic, and sentiment on microblog conversation summarization is worth exploring in future study.


Second, the gold labels for the sarcastic class are obtained differently: Whereas Twitter and Reddit corpora are self-labeled (i.e., speakers themselves label their messages as sarcastic), the IAC v2 corpus is labeled via crowdsourcing.

for the latter, the gold labels emphasize whether the sarcastic intent of the speaker has been perceived by the hearers/annotators (we do not know if the speaker intended to be sarcastic or not).


Only in the case of IAC v2 , for tf-idf ct+pt bl setting, did the RBK kernel perform better (68.15% F1 for category S and 64.56% F1 for category NS).

we only report the performance of the linear kernel for all the experiments.


Although, the IAC v2 and Reddit corpora are annotated differently (using crowdsourcing vs. self-labeled, respectively), the labels are for the posts and not for specific sentences.

for longer posts, often the LSTM models perform poorly because the sarcastic cue is buried under the remaining non-sarcastic parts of the post.


Here the model picked up the opposite sentiment from the P TURN and C TURN, that is, "chose to fight" and "protecting home from looter."

the model seems to learn the incongruity between the prior turn P TURN and the current turn C TURN regarding the opposite sentiment.


Identifying polarity contrasts between implicit and explicit evaluations, on the other hand, seems to be more difficult, resulting in a moderate Kappa of 0.55.

once the annotation of the entire corpus was completed, all annotations of implicit sentiment were reconsidered by one of the experts.


The knowledge base contains mainly unigrams, bigrams, and trigrams, so most targets contain more words than would fit in a single query to the database (see Table 8).

in the case of a target being a multiword expression or a phrase, the overall polarity had to be defined based on the polarities of the individual words in the target.


The fourth and final step amounts to constructing topic-dependent BAFs from reviews, for the purpose of assessing how "good" the item being reviewed is by assessing the dialectical strength of a special argument G (for "good") in the constructed BAFs.

in turn, each topic t identified at Step 2 gives a special argument G t (for "good as far as t is concerned") supporting G. intuitively, the stronger the various G t in the computed BAFs, the stronger the G. in addition to these special arguments G t supporting G, the BAFs also include relations between arguments related to topic t drawn from reviews and G t , so that a newer argument (with respect to time) can either support, attack, or neither support nor attack a previous argument or G t , but not vice versa.


By visualizing the type of conversations found by the two models, they show that the output of the HMM+Topic model is more interpretable than that of the HMM one; however, their classification accuracy is not empirically evaluated.

it is not clear whether these models are actually useful, and which of the two models is a better speech act tagger.


Additionally, the human scores also suggest an asymmetric impact of the non-literal parts over the compound: whenever participants judged an element of the compound as non-literal, the whole compound was also rated as idiomatic.

most head and modifier scores (hc H and hc M ) are close to or above the diagonal line in Figure 1(b).


The goodness of fit is r 2 arith = .93 for the arithmetic mean, and r 2 geom = .96 for the geometric mean, confirming that they are good predictors of hc HM .

14 we assume that hc HM summarizes hc H and hc M , and focus on predicting hc HM instead of hc H and hc M separately.


Not all compounds in the English data sets are present in our corpus.

we also report results adopting a fallback strategy (the second value).


They may also be a feature of this particular set of compounds, as even the compositional cases are also conventional to some extent (e.g., white/?yellow wine).

further investigation of possible links between idiomaticity and conventionalization is needed.


Our extraction algorithm integrates the key idea underlying LFG and the practice of corpus annotation of CTB.

the structures obtained are highly comparable to Dalrymple (2001)'s de facto LFG analysis.


Our corpus is based on the original annotations of CTB, which is based on the GB theory.

our grammatical function-augmented trees include unpronounced nodes, and there are not many functional schemata associated with constructions or function words.


Since the consistency tags emerge, we index the graph and tree vector representation using three indices for convenience.

g g g(i, j, t) denotes whether there is an edge from word w i to word w j with tag t in graph g. The joint decoding problem can be written as a constrained optimization problem as where g g g k = t2g(g g g k )(k = 1, 2, 3).


If we take a a a 12 , a a a 13 , • • • , a a a 32 as constants, then all the constraints are linear.

the constraints can be written as A 1 g g g 1 + A 2 g g g 2 + A 3 g g g 3 = 0 0 0 where A 1 , A 2 , and A 3 are matrices that can be constructed from a a a 12 , a a a 13 , • • • , a a a 32 .


3 Second, if two alignment columns are identical, they must reflect the same proto-sound, if alternative processes like borrowing can be excluded.

similarly to the prediction of missing words in our cognate sets, we could use correspondence patterns to infer proto-forms, provided that parts of the data are already annotated.


19 The quantitative treatment of sound correspondence patterns presented in this study is novel.

no expert-annotated data listing all observable correspondence patterns for a certain language family exhaustively is available.


While the two former data sets are classical wordlists that are further coded for cognacy and alignments, 21 the Chinese data is based on a collection of 623 morphemes (reflected by a Chinese character each) whose pronunciation across the 14 dialects used in our sample was elicited by field workers.

the amount of cognate sets with missing reflexes in this data set is extremely low.


The responses from the chatbot may drift to the topic of "Shanghai" if the chatbot pays significant attention to these words.

it is crucial yet non-trivial to let the chatbot understand the important points in the context and leverage them in matching and at the same time circumvent noise.


The context is represented without enough supervision from the response, and so is the response.

these models may lose important information of contexts in matching, and more seriously, no matter how we improve them, as long as the improvement is under the framework, we cannot overcome the limitations.


In both sets, a context has a human reply as a positive response and a randomly sampled reply as a negative response.

the ratio of positive instances and negative instances in both training and validation is 1:1.


One is that response candidates in the Douban corpus are returned by a search engine rather than negative sampling.

some negative responses in the Douban corpus might be semantically closer to the true positive responses than those in the Ubuntu corpus, and thus more difficult to differentiate by a model.


Not only the modern language C but also its ancestor P and the intermediate languages a smooth line of natural languages must be drawn between a modern language and its ancestor, and by extension, between any pair of phylogenetically related languages, as illustrated in Figure 2.

we expect languages to lie on a smooth lower-dimensional manifold.


These priors are not non-informative, but they are sufficiently gentle in the regions where these model parameters typically reside.

an extension of the model is to set z * ,K = (1, • • • , 1) for the last linguistic parameter K. the autologistic model is dropped from the linguistic parameter K (while K − 1 autologistic models remain).


In other words, discerning between physical and social dimensions is ambiguous.

starting from the original complex and extremely fine-grained classification, we worked to find an appropriate level of granularity by merging categories with a common conceptual core.


the noun "trip" is annotated as noun.act whereas the verbs "to trip" and "to travel" are annotated as verb.motion.


When normalizing text for speech synthesis, however, the written numeric value must be mapped to its spoken form (e.g., one hundred ninety seven or one nine seven), which is often called number naming.

in the parsing scenario the normalization is deterministic; in the speech synthesis scenario it is contextually ambiguous.


The first component, tokenization, can be treated with hand-written grammars, but this may be brittle and challenging to maintain.

it seems desirable to use machine-learned taggers, similar to those used to segment text in writing systems that do not separate words with spaces; see, for example, Wang and Xu (2017), who use convolutional neural networks for Chinese word segmentation.


In contrast, there is no reason why people would spend resources producing verbalized equivalents of ordinary written text: in most cases, English speakers do not need a gloss to know how to read $10 million.

if one wants to train neural models to verbalize written text, one must produce the data.


Tokens in Kestrel need not correspond to whitespace-delimited tokens, even in those languages where whitespace or punctuation is used to separate words.

the Kestrel grammars recognize Jan.1, 2012 as a date and parse it as a single token, identifying the month, day, and year, and represent it internally using a protocol-buffer representation like the following: 6 date { month: "January" day: "1" year: "2012"} Verbalization grammars then convert from a serialization of the protocol buffer representation into actual word sequences, such as January the first twenty twelve.


Namely: Why not treat the text normalization problem as a machine translation task, where the source language is raw text and the target language is normalized text, in the same language as the source language?

in our case a source sentence might be John lives at 123 King Ave next to A&P.


In data sources such as that in Wikipedia perhaps as many as 99% of the examples of the form month-day-year have a four-digit year, reflecting a bias toward events occurring after AD 1000.

it is not surprising that the neural system would have a bias to interpret something as a four-digit date in such contexts, even if the input is a threedigit date.


Thus the input i is composed with Map, which is then composed with D, which is composed with the output o. Analogously to the induction of number name grammars described above, since we wish to extract the best alignment leading to the best rule from the instance in question, we compute the shortest path P that lies in the intersection of the output of Map and the input of D, but here we need to preserve the input side as well, since what we need in this case is to learn a transducer that maps between the markup and sequences of tagged spans, and so we do not want to project to the output of i•Map.

: Because of the large number of entities that need to be remembered by the PDT, the approach is not practical in this case.


Furthermore, the system of Pramanik and Hussain (2018) continues to make unrecoverable errors, though they do claim that their solution fails to make such errors in DATE examples like 11/10/2008; and while Yolchuyeva, Németh, and Gyires-Tóth (2018) do not report whether their system makes these kinds of errors, given that their overall per-class errors do not differ substantially from our own, it would be surprising if such errors did not occur.

it seems reasonable to conjecture that one is not going to eliminate the problem of unrecoverable errors by simply choosing a different model architecture.


Turning to the Kaggle test set, the CARDINAL errors with the CG were mostly cases where the CG in fact corrected an error made by the neural model alone, but still got the morphological form wrong.

200 was predicted by the neural model to be ('null'), and the CG corrected this to 'two hundred' (genitive case), whereas the reference form was 'two hundred' (nominative/accusative case).


In a similar way, all MONEY CG errors involved morphological case differences.

£4 is verbalized as 'four pounds sterling' (instrumental case on the number) rather than (genitive); the neural net produced ('four four') on its own.


More precisely, in graph parsing the input graph is fully specified by means of its vertices and edges, whereas in string-to-graph parsing we do not have access to the edges of the graph.

in this respect, string-to-graph is more general than graph parsing.


By the inductive hypothesis an in-order traversal of The remaining cases for an internal node n can be dealt with in a similar way.

finally, from this property it follows that the set of vertices introduced by the anchored nodes within T n is [i, j], since [i, j] contains all and only the symbols Definition 2 places constraints on the tree structure of the tree decomposition T, based on the ordering w, but does not refer to the contents of the bag at each node of T. the problem of finding a tree decomposition of G can be broken into the two problems of, first, finding a parse tree for w and, second, identifying the vertices of G to include in the bag at each node of T. Notation.


But, in an inside tree decomposition, a vertex can only appear along a path from one node up toward the root.

the structure shown in T cannot appear in an inside tree decomposition, and thus no inside tree decomposition can achieve the optimal width tw(G).


Each bag in T * must have the contents assigned by Algorithm 1 by the first part of Theorem 1.

by induction over nodes n in T * in bottom-up order, with [i, j] the span of n, each quantity b(i, j) at the completion of the inner loop at line 6 can be no larger than the maximum bag size of the subtree of T * rooted at n. the treewidth returned by Algorithm 1 can be no larger than the treewidth of T * .


There must exist a vertex v such that u ∈ N(v), with T(v) below n and T(u) dominating T(v).

v ∈ [i, j], while u ∈ [i, j], and therefore u ∈ O(i, j).


More precisely, the tree decomposition has the same nodes and the same branching structure as the derivation tree, and each bag in the tree decomposition contains all the vertices that appear in the right-hand side of the rule.

the derivation tree must satisfy the three properties in the definition of a tree decomposition: Vertex cover: Each vertex in G must be generated by some rule in the HRG derivation tree t. Edge cover: Each edge (u, v) in G must also be generated by some rule r, and therefore both u and v appear in the right-hand side of r, and in the node for r in t. Running intersection: Any vertex v of G is first generated by some rule r of t. Vertex v can only appear in another rule r if it is connected to the lefthand side nonterminal of r , and therefore appears in the parent of r in t. all occurrences of v form a connected subtree of t. See also Lautemann (1988) for further discussion.


This is likely to bias representations by speakers from different countries talking about specific issues or places (as in Figure 2), and gives the model comparatively little information to work with as there is no explicit syntactic information available.

of the lack of explicit syntactic information, it is unsurprising that the results (LM-Raw in Table 1) only marginally outperform the random baseline.


However, frame-semantic resources are arguably expensive and time-consuming to build because of difficulties in defining the frames, their granularity and domain, as well as the complexity of the construction and annotation tasks.

such resources exist only for a few languages (Boas 2009) and even English is lacking domainspecific frame-based resources.


Having chosen cosine similarity as the context similarity measure, we compute the similarity between ctx(bank 2 ∪ {bank}) and the context of every sense of building in Table 3: cos(ctx(bank 2 ) ∪ {bank}, ctx(building 1 )) = 2 3 and cos(ctx(bank 2 ) ∪ {bank}, ctx(building 2 )) = 0.

for the word building in the Matching the meaning of the ambiguous node "building" in the context of the sense bank 2 .


Because of the use of a hard clustering algorithm for node sense induction (Section 2.2), in any pair of nodes {u, v} ∈ E, the node v can appear in the context of only one sense of u and vice versa.

we can omit the context disambiguation step (Section 3.2.2) by tracking the node sense identifiers produced during sense induction.


Let senses max be the maximum number of senses for a node and ctx max be the maximum size of the node sense context.

this operation takes O(|V| × senses max × ctx max ) steps to iterate over all the node sense contexts.


This requires O(senses max × ctx max ) steps per each node in context.

the whole operation takes O(|V| × senses 2 max × ctx 2 max ) steps.


Then, for every pair of gold standard data sets, we computed the pairwise precision, recall, and F 1 -score by assessing synset-induced synonymy pairs of one data set on the pairs of another data set.

we see that the low absolute numbers in evaluation are due to an inherent vocabulary mismatch between the input dictionaries of synonyms and the gold data sets because no single resource for Russian can obtain high recall scores on another one.


and establish cosine similarity-weighted edges between the corresponding triples.

we establish edges only between the triples appearing in k nearest neighbors (lines 3-4): the constructed triple graph G has a clustered structure in which the clusters are sets of SVO triples representing the same frame.


Finally, for each cluster C i ∈ C, we aggregate the subjects, the verbs, and the objects of the contained triples into separate sets (lines 7-9).

each cluster is transformed into a triframe, which is a triple that is composed of the subjects f s ⊆ V, the verbs f v ⊆ V, and the objects f o ⊆ V. For example, the triples shown in Figure 9 will form a triframe ({man, people, woman}, {make, earn}, {profit, money}).


Also, because of the difference in lexicons between the gold standards and the input graph, we performed additional pruning by removing all the edges connecting words missing the gold standard lexicons.

we obtained four different pruned input graphs (Table 18).


That is to say, for example, that metrics that perform the best for evaluating adequacy also perform the best for evaluating fluency.

the results from meta-evaluation with a particular type of manual assessment can be more often than not extrapolated to other quality aspects.


matching the reference is less clearly separated from the rest of the data.

6 a simple visual inspection of the scatter plots already provides some insights into the metrics' behavior.


This is not surprising because, as mentioned before, multiple confounding factors are involved, including the difficulty of predicting a particular type of judgment, the overall level of MT quality, the reliability of manual assessments, and so on.

the results in Table 10 should not be compared directly but rather in relative terms, that is, by seeing whether the ordering of the metrics based on the correlation with human assessments is similar for different data sets, annotated with different labels.


WMT16 and EAMT11 also correlate well, although manual evaluation in EAMT11 is based on the post-editing effort criterion.

the metrics that best predict adequacy seem to be the ones that also best predict fluency and post-editing effort.


We have seen that even though metric scores may vary considerably depending on the domain, type of human reference translation, language pair, and so forth, the relative difference in evaluation accuracy is maintained across different data sets and types of manual assessments.

meta-evaluation results obtained using a particular type of assessment can generally be extrapolated to other evaluation scenarios.


Grade 9 and 10 data are used as unlabeled data.


We add some more entities, functions, and predicates to cover other more complex concepts in geometry not covered in GEOS.

we obtain a final set of 19 entity types and 115 functions and predicates for our parsing model.


We can observe that removal of any of the features results in a loss of performance.

all the content as well as typographical features are important for performance.


False cognates are more harmful than missing valid cognates in language comparison, because they can lead to incorrect conclusions regarding the genetic relationships between languages (List, Greenhill, and Gray 2017).

the need for discriminating between cognates and borrowings emerges.


Because this word does not have Italian etymology, we assume it might have a cognate pair in Italian.

we translate it in Italian, obtaining the word vittoria.


We account only for lexemes, leaving inflected form aside.

we consider 136,733 words in our investigation.


10 Its main idea is that any partial path of the alignment along the optimal path should be the optimal path leading up to that point.

the optimal path can be determined by incremental extension of the optimal subpaths (Schuler 2002).


In order to provide information regarding the position of the features, we mark the beginning and the end of the word with a $ symbol.

for the above-mentioned pair of cognates, (exhaustiv, esaustivo), we extract the following features around any type of mismatch in the alignment, when n = 2: x>s ex>es xh>s-h>-xh>s-ha>-a ->o v->vo -$>o$ For identical features we account only once.


Thus, for the above-mentioned pair of cognates, (exhaustiv, esaustivo), we extract the following features around any type of mismatch in the alignment, when n = 2: x>s ex>es xh>s-h>-xh>s-ha>-a ->o v->vo -$>o$ For identical features we account only once.

because there is one feature (xh>s-) which occurs twice in our example, we have eight features for the pair (exhaustiv, esaustivo).


As the example shown in Figure 5, r 72 for fantastic nsubj − −−− → software and r 82 for horrible nsubj − −−− → staff are close to each other.

these relation vectors could be regarded as a bridge for word knowledge transfer across domains: The hidden representations of the parent words (h 7 for fantastic and h 8 for horrible) for these relations are aligned, irrespective of the domains.


Moreover, g ij is also used as the higher-level feature vector for r ij for predicting the relation label.

the objective for the auxiliary task in Equation (4) becomes: where Here R 1 is the reconstruction loss with W dec being the decoding matrix, R 2 follows Equation (4) withŷ R ij = softmax (W R g ij + b R ), and R 3 is the regularization term on the correlations among latent groups with I being the identity matrix andḠ being a normalized group embedding matrix that consists of normalized g k s as column vectors.


It may be argued that although the proposed model is not trained on parallel data, it takes advantage of the fact that the textual resources used for development come from the same sources as the test data (i.e., Wikipedia).

the better performance can be attributed to having a better vocabulary coverage (covering more entities, verbs, nouns, etc.


In cases where context is not adequately captured, a model trained on one set of data can struggle to classify spans in another set of data where the context is different.

rule-based and unsupervised learning approaches have also been applied to this task.


For the present purposes of using ASJP for distinguishing languages and dialects, we are only interested in comparing genealogically related language varieties.

we can resort to the simpler and faster LDN measure.


The corresponding temporal distance lies around 1,355 years, within the interval 1,075-1,635 years.

we now have a principled way of distinguishing languages and dialects.


For instance, from a single noun a minimum of 135 inflected forms can be generated.

the number of simple word-forms covered by the current 70,000 dictionary entries would not be less than 10 million.


The result is the set of possible morphological segmentations of a word, where each morpheme is associated with its corresponding features in the lexicon: part of speech (POS), subcategory, declension case, number, definiteness, as well as syntactic function and some semantic features.

the output of the segmentation phase is very rich, as shown in Figure 2 with the word amarengan (Eng.


Alternatively, a model with minimal morphotactic treatment (Ritchie et al.,92) would produce too many possible analyses after segmentation, which should be rejected in a second phase.

we propose to separate sequential morphotactics (i.e., which sequences of morphemes can or cannot combine with each other to form valid words), which will be recognized by the two-level system by means of continuation classes, and non-sequential morphotactics like long-distance dependencies that will be controlled by the word-grammar.


The hypotaxis means that there is a depend-ency of subordination between the involved syntactic items, while the parataxis is neutral in this respect and is regarded as the unmarked member of the taxis opposition.

if two linguistic entities belong together from the viewpoint of structural syntagmatics, they are involved in one of the following relation types which are obtained via admissible crossclassifications (Hierarchy 4).


The class NS contains both masculine and neuter nouns, and these differ in none of their inflected forms.

only agreement information from a context, such as der enge Sund 'the narrow strait' (nominative), can establish the gender of Sund as being masculine.


For example, if Root selects the NP in figure  1, then the f-structure corresponding to the S node is erased, giving figure 2 as a possible fragment: In addition the R o o t operation deletes from the remaining f-structure all semantic forms that are local to f-structures that correspond to erased c-structure nodes, and it thereby also maintains the fundamental two-way connection between words and meanings.

if Root selects the VP node so that the NP is erased, the subject semantic form "Kim" is also deleted:  As with Tree-DOP, the Frontier operation then selects a set of frontier nodes and deletes all subtrees they dominate.


Sampling a random derivation from the chart consists of choosing at random one of the fragments from the set of composable fragments at every labeled chart-entry (in a top-down, leftmost order so as to maintain the LFG-DOP derivation order).

the competition set of composable fragments is computed on the fly at each derivation step during the Monte Carlo sampling process by grouping the f-structure units that unify and that are coherent with the subderivation built so far.


The proposed parse is incorrect since it has the incorrect feature value for the TENSE attribute.

if this were the only test sentence, the exact match would be 0%.


(1997) and Sima'an (1999) report that (probabilistic) DOP suffers considerably from data-sparseness on OVIS, yielding a decrease in parse accuracy if subtrees larger than depth 4 are included.

it is interesting to investigate how nonprobabilistic DOP behaves on this corpus.


Additional 15.2% of failures could also be avoided if the summarizer was capable of detecting sub-stories within a single section, leading/trailing noise (see below), and so forth.

almost half of the errors (in a certain summarization regime, at least) could have been avoided by using a segmentation component.


We also make arrangements for a mechanism whereby certain strategies for incorporating segmentation results into the summarization process were easy to cast in summarizer terms.

for instance, a heuristic requiring that each segment is represented in the summary can be naturally expressed by treating segments as sections, and strictly enforcing the 'empty section' rule (see 2.2).


Here we highlight a few observations concerning the overall benefits that segmentation brings to summarization.

in addition to facilitating sentence-based summaries with certain discourse and rhetorical properties, it turns out that under certain conditions the summarizer can operate very effectively without a need for background corpus statistics.


This means that while language-independent sentence alignment programs typically achieve a recall in the 90 percent range, the same cannot be said about word alignment systems, where normal recall figures tend to fall somewhere between 20 and 40 percent, in the language-independent case.

we need methods to increase word alignment recall, preferably without sacrificing precision.


alignment always works with sentence tokens, 2 i.e., it relies on linear order.

this means that polysemy (one type in the SL corresponding to several types in the tL), homonymy (several types in the SL corresponding to one type in the tL), and combinations of polysemy and homonymy will disrupt the correspondence even between structurally similar languages; the circumstance that linear order cannot be used to constrain word alignment -beyond the restriction that putative word alignments must appear in one and the same sentence alignment unit-together with the other factors just mentioned, conspire to make word alignment a much harder problem than sentence alignment in the language-independent case.


A new sampling of the same corpus was performed, but this time we first constructed a stop word list consisting of the 50 most frequent word types in the Swedish part of the IVT1 corpus, as a language-independent way of approximating the set of function words in the language.

we had a new sample, with more content words, to compare with the previous one, the hypothesis being that a larger percentage of content words would be able to contribute more links in the pivot alignment process.


For the same reason, we do not have all the data needed to calculate the significance of the results.

the results will have to remain suggestive for the time being, although the suggestion is strong that pivot alignment works the way it was hypothesized to work.


These results were obtained with some accessory devices: Each node in the tree is "conceptualized as a pair consisting of a tree and a vertex in that tree" (p.62).

the whole tree where a given NP appears is locally accessible to be "walked up" since its replica is present at the pair (Category, Tree), which is the NP node itself.


The level of equivalence can be taken as a limited interaction test: These test cases represent one complete selection of grammar disjuncts, and given the grammar there is nothing we can gain by c hecking a test case if an equivalent one was tested.

this level of redundancy may be used for ensuring the quality of grammar changes prior to their incorporation into the production version of the grammar.


It may be di cult, time-consuming, and expensive to obtain that much parallel text, particularly for lesser-used language pairs.

if one wishes to develop a new translator rapidly and at low cost, techniques are needed which permit the EBMT sys-tem to perform just as well using substantially less example text.


On its own, clustering is quite successful for generalizing EBMT examples, but the fullyautomated production of clusters is not compatible with adding a production-rule grammar as described in Brown, 1999.

the clustering process may be seeded with a set of manually-generated clusters.


Second, whenever a tagged cluster is merged with an untagged one or another cluster with the same tag, the combination inherits the tag; further, merging two clusters with di erent tags is disallowed.

the initial seed clusters are expanded by adding additional word pairs while preventing any of the seed clusters from themselves merging with each other.


Since the OHFD has been built using corpora frequencies, the most frequent senses of a word appear first in the entry.

even if there are no matching rules, the system gives as result the most probable meaning of the word to disambiguate.


which involve seize, are: SUBJ(police,seize) DOBJ(seize,man) VMODOBJ (seize,about,1985) VMODOBJ(seize,of,Society) VMODOBJ(seize,by,branch) In the case of our example, none of the rules of the database match directly the lexical context of the dependencies.

the system tries to match the semantic context of the dependency.


In this way, there is at most one corresponding news cluster in another language.

the main task of the multilingual news clusterer is to find the matchings among the clusters in different languages.


The word "º|" (although) is a forward linking element.

the first two segments are connected together (C2).


Movement transformation may affect the order of subject-verb-object.

in M3 we give up the order criterion, but we add an extra score when continuous terms are matched, and subtract some score when the object of a transitive verb is not matched.


There is no way of collecting every words of a language, since new words will be created for expressing new concepts, new inventions.

how to identify new words in a text will be the most challenging task for natural language processing.


Since for the majority of compounds the presupposition hold, the design of our semantic classification algorithm will be based upon this presupposition.

the process of identifying semantic class of a compound boils down to find and to determine the semantic class of its head morpheme.


We might not expect lightning to strike twice, but it happens all the time, especially for good keywords.

far, we have seen that adaptation can be large, but to demonstrate the shape property (lack of dependence on frequency), the counts in the contingency table need to be smoothed.


Note that the ns fall between the ps and the hs.

far, we have seen that adaptation can be huge: Pr( + adapt) >> Pr(prior), often by two or three orders of magnitude.


If c 0 is a kind of c, then c is a hypernym of c 0 , and c 0 a hyponym of c. Counts are passed up the hierarchy from the senses of nouns appearing in the data.

if eat chicken appears in the data, the count for this item passes up to hmeati, hfoodi, and all the other hypernyms of that sense of chicken.


Let isa = direct-isa be the transitive, re exive closure of direct-isa, so that c 0 ; c 2 isa c is a hypernym of c 0 ; and let c = f c 0 jc 0 ; c 2 isa g be the set consisting of the concept c and all of its hyponyms.

the set hfoodi contains all the concepts which are kinds of food, including hfoodi.


The original corpus-based method of Hindle and Rooth 1993 used exactly this information.

the method described here can be thought of as Hindle and Rooth's method with additional classbased information about n 2 .


It is possible, for a given referent a and a given k, that no co-referential link exists in the units of the corresponding EDRA k .

in this case, we consider that the effort is equal to k. for small ks the effort required to establish co-referential links is similar for both theories, because both can establish only a limited number of links.


The first, possibly most serious, limitation originates in the fact that a standard DTD is severely restricted in the semantic dependencies it can express between two subtrees in the document structure.

if in the description of a contact, a city of residence is included, one may want to constrain such an information depending on the country of residence; or, in the aircraft maintenance manual example, one might want to automatically include some warning in case a dangerous chemical is mentioned somewhere else in the document.


Although the most common use of an adverb modifying an adjective is to function as an intensifier or diminisher (Quirk et al., 1985, p. 445), adverbs can also add to the semantic content of the adjectival phrase instead of providing a grading effect (e.g., immediately available, politically vulnerable), or function as emphasizers, adding to the force of the base adjective and not to its degree (e.g., virtually impossible; compare *very impossible).

we compiled by hand a list of 73 adverbs and noun phrases (such as a little, exceedingly, somewhat, and very) that are frequently used as grading modifiers.


The third column gives the number of subjective sentences that contain one or more instances of members of Ë, and the fourth column gives the same figure for objective sentences.

these two columns together specify the coverage of the subjectivity indicator examined.


(1) X has an antecedent beyond a segment boundary def description (2) X has an antecedent two or more utterances distant def description (3) X has an antecedent in (u1), and (3a) X occurs in strong parallel context pronoun (3b) X / ∈ f ocus def description (3c) X ∈ f ocus and • X has a competing referent Y ∈ f ocus def description • X has a competing referent Y in (u1) amplified with apposition or nonrestrictive relative clause def description • else pronoun The repetition blocking rule overrides the pronominalization suggested in (3c) to a definite description.

figure 2: The algorithm pronoun repetition blocking seems to be an aggregation trigger rather than a motivation for definite description generation.


We also changed some POS tags for functional words like prepositions and words such as `be' and `have' to tags which denote their literal forms, because the choice of POS of a focus word is highly dependent on the word itself.

we obtained 513 POS tags including 16 POS tags for nouns, 17 for verbs, 410 for prepositions and phrasal prepositions, and 70 for adjectives and adverbs.


These rules are not used in the next experiment.

we eliminated the extracted 17,731 rules whose adjustment ratio are below 60% and obtained 4,494 rules such as : where ADJ=adjective, ADV=adverb, CC=coordinate conjuction, DET=determiner, DEM=demonstrative, NAMEP=place name, N=noun, NAME=proper noun, NT=noun meaning "time", NU=noun meaning "unit", PP=past particle, PNC=punctuation mark other than commas, V=verb (other than past form), VP=verb (past form).


This means that if A and C give dierent answers with the same DP, C's answer is more likely to be correct.

it is not necessarily a good strategy to simply use given bare DPs in weighted majority.


Since, in Japanese, a BP always depends on a BP following it, the right-most BP of a sentence does not depend on any other BP, and the second right-most BP always depends on the rightmost BP.

they were not seen as subjects of evaluation.


A stochastic top-down parser using the PCFG estimated from the trees produced by T L simulates a stochastic generalized left-corner parser, which i s a generalization of a standard stochastic left-corner parser that permits productions to be recognized top-down as well as left-corner Manning and Carpenter, 1997.

investigating the properties of PCFG estimated from trees transformed with T L is an easy way of studying stochastic push-down automata performing generalized left-corner parses.


One point lies in the new distance metric.

(3) is the probability that n chooses the ith candidate as the modifiee among the modification candidates which are ordered according to their distance to n. we no longer require the distance metric ∆ n,h , instead we use the relative position among the modification candidates, which works as an attribute of the modification.


(1999) report that when modification candidates are enumerated according to SLUNG, 98.6% of the correct modifiees are in one of the following three positions among the candidates: the nearest one from the modifier, the second nearest one, and the farthest one.

we can simplify the problem by considering only these three candidates and discarding the other candidates, with only 1.4% potential errors.


Actually, the grammar allows Taro-no to depend on either of these types of words.

in our model, Pa(Taro-no → musume) = P (2|Taro-no, kawaii, musume) P b (Taro-no → musume) = P (2|Taro-no, yuujin-no, musume) Then, P (Taro-no →musume) has different values for the two examples.


But it is not feasible because transliterated words are usually technical terms and proper nouns that have rich productivity.

an automatic transliteration system is needed to nd transliterations without manual intervention.


The proof of the theorem is constructive: it indicates precisely how to build the grammar G F whose language is the desired set of strings.

the problem of LFG generation is divided into two phases, constructing the context-free grammar G F , and then using a standard context-free generation algorithm to produce strings from it.


In case of Japanese, however, syntactic analysis cannot clarify relations between words in sentences because of several troublesome characteristics of Japanese such as scrambling, omission of case components, and disappearance of case markers.

in Japanese sentence analysis, case structure analysis is an important issue, and a case frame dictionary is necessary for the analysis.


Such a consideration must be done considering what other case components modify V c and V m , since the assigned case slot of a case component might differ depending on the candidate structure of the sentence due to the unique case assignment constraint.

it is necessary to expand the structural ambiguity and consider all the possible structures for an input.


Then, we compared the parse results of our method with those of the original KNP.

565 modifier-head relations differed; in 260 cases, our method was correct and the original KNP was incorrect (by considering the structures in the Kyoto University Corpus as a golden standard); in 224 cases, vice versa.


c) extracting linguistic information on a large scale such as lexical preferences for verb subcategorization frames.

(cf Kinyon (99a)) d) Building an efficient, but nonetheless psycholinguistically motivated, processing model for TAGs (cf Kinyon 99b)in addition of being well-defined computational objects (Point a), hypertags should be "readable" (point b) and also motivated from a linguistic point of view (Points c & d).


For example, if one considers the trees α1 and β1 from figure 1, it is obvious that they hardly have any structural information in common, not even the category of their root.

representing these 2 structures in a graph would not help.


The key idea then is to represent a set of elementary trees by a disjunction for each dimension of the hierarchy.

a hypertag consists in 3 disjunctions (one for dimension 1, one for dimension 2 and one for dimension 3).


With hypertags, each lexical entry is now paired with one unique structure.

automatically hypertagging a text is easy and involves a simple dictionary lookup.


The proposed method shows a similar degree of performance, compared with the traditional supervised learning methods.

this method can be used in areas where low-cost text categorization is needed.


With the rapid growth of the internet, the availability of on-line text information has been considerably increased.

text categorization has become one of the key techniques for handling and organizing text data.


But a sentence is a processing unit in the proposed method.

the document frequency cannot be counted.


The results of top 10% and top 15% were best.

we used the maximum in the decision of similarity value and top 15% in threshold in our experiments.


The experiment results showed that with respect to performance, the difference between the proposed method and the method by supervised learning is insignificant.

this method can be used in areas where low-cost text categorization is required, and can be used for creating training data.


On the other hand, in more complicated task such as information retrieval, the vocabulary of the next utterance cannot be limited on all occasions, because the user should beable to input the values in various orders based on his preference.

without imposing a rigid template upon the user, the system must behave appropriately even when speech recognizer output contains some errors.


It is not usual that users change the already speci ed slot values.

recognition results that overwrite lled slots are likely to be errors, even though its C M w is high.


It is observed that semantic-attribute CMs are estimated more correctly than content-word CMs.

even when successful interpretation is not obtained from content-word CMs, semantic-attribute can be estimated correctly.


A language L has the bounded g r owth property if (and only if) L is a singleton or 9k 2 IN = 8w 2 L ; 9w 0 2 L n f wg = jw 0 j 0 j wj k Now, it is easy to prove (see Appendix) that: Theorem 7 Any language of analogical strings veries the bounded g r owth property.

a language like fa 2 n =n 2 INg is not a language of analogical strings, as it does not have the bounded growth property.


The Similarity constraint implies: jw 0 j 0 sim(w 0 ; w ) = jv 0 j 0 sim(v 0 ; v ) jwj 0 sim(w 0 ; w ) = jvj 0 sim(v 0 ; v ) Because sim(w;w 0 ) min(jwj; jw 0 j) is always true: jw 0 j 0 j wj = max(jwj; jw 0 j) 0 min(jwj; jw 0 j) max(jwj; jw 0 j) 0 sim(w;w 0 ).

: 8w 2 3(A; M); 9w 0 2 3(A; M)nfwg = jw 0 j 0 j wj max(jw 0 j 0 sim(w;w 0 ); jwj 0 sim(w;w 0 )) max(jv 0 j 0 sim(v;v 0 ); jvj 0 sim(v;v 0 )) By taking k the maximum over all v !


ASR is a dialogue quality measure that approximates speech recognition accuracy for the database query, and is computed by adding 1 for each correct attribute value and .5 for every wildcard.

if the task is to go winetasting near Lambertville in the morning, and the system queries the database for an activity in New Jersey in the morning, StrongComp=0, WeakComp=1, and ASR=2.


Instead, a weighted average of the actual output and the desired output is used: where DES is the desired output, DES = ( D 1 ; D 2 ; 1 11 ; D ); 12whose bits are dened as and w OPT and w DES are respectively dened as and w DES = 1 0 w OPT ; (15) where E OBJ and E ACT are the objective and actual errors.

at the beginning of training, the weighting of the desired output is large.


The training should be stopped at an appropriate level of accuracy.

neural net may not acquire some useful rules.


Nevertheless, as we have seen in the examples discussed in this paper, the fact that a paratactic relation holds between spans does not imply that there exists a satisfactionprecedence relation at the intentional level between those spans.

for satisfaction-precedence relations, we will have only one axiom, that shown in (10), below.


In this case, a 1-to-1 mapping transformation results in an unnatural translation.


For an MWTU, "-ji(mC,2) an(V,0), the recognition scope of "an" is 1 because d 1,2 is 2 and real_dist 1,2 is 1, which is from 12-11.

we can recognize MWTUs by a small comparison.


Bunsetsu identication is treated as the task of deciding whether to insert a \j" mark to indicate the partition between two bunsetsus as in Figure 1.

bunsetsu identication is done by judging whether a partition mark should be inserted between two adjacent morphemes or not.


In such a case, the system should change its description or ask the user to change her his view to the left side view Figure 2 and recommence its instruction about this part.

the system should be able to change the content of the dialogue according to the user's view.


The advantage of this method is that the plan is developed incrementally, and can be changed while the conversation is in progress.

by applying this algorithm to 3D dialogues, it becomes possible to set instruction dialogue strategies that are contingent on the user's view.


If an object is bound to the ?object variable by referring to the knowledge base, and the object is visible to the user, hOperator 1i is selected.

two Main-Acts looking at the user and requesting to try to do the action and two Subsidiary-Acts showing how to do the action, then resetting the state are set as subgoals and returned to the DM.


In order to answer the question, the DM asked the Domain Plan Reasoner how to answer the user's question.

a discourse goal was returned to the DM and added to the agenda.


After returning to the previous view, the interrupted goal was re-planned.


At this time, if the user is viewing the left side Figure 2 and the system has the goal Instruct-act S H remove-tierod end r MM, hOperator 2i in Figure 4 is applied because the target object, right tie rod end, is not visible from the user's viewpoint.

a goal of making the user view the right tie rod end is added as a subgoal and utterances 24 and 25 are generated.


In more technical terms, the set is obtained by the summation of the values which 'y' can take.

there is no direct way of mapping a plural object in a semantic network (which represent the interpretation of an NP) to a plural discourse referent in the corresponding DRS.


Both are considered to be individuals: atomic and non-atomic individuals, respectively.

the model theory follows the models which Link (1983) provides for count nouns.


As a match between C i and I j occurs when "stepping across a diagonal", the (m-1) first diagonals (from the lower left corner of the array) can not give birth to a valid path.

we do not calculate d [i,j] across these small diagonals.


After tactical generation, we might obtain the following (rather poor) result: Elixir contains gestodene.


We do this by using linear precedence rules LP-rules, most of which place constraints on immediate local subtrees.

we can say things like fA; B; Cg : +wh@A&,wh@B !


A correct frame (verb + its arguments) is almost always accompanied by one or more adjuncts in a real sentence.

the observed frame will almost always contain noise.


Nevertheless, for each observed frame, one of its subsets was the correct frame we sought for.

we considered all possible subsets of all observed frames.


BNC The decisive factor seems to bethe verb sense involved.

for disambiguation a list of such immune verb senses must becompiled.


The heuristic assumption is that if a time adverbial is to designate a long period more detail is given in the NP argument.

the relevant temporal prepositions map the noun granularity to the next higher level on a certain scale day month y ear eternity.


If we analyse sentence 23a in this vein, we get 23b as logical representation: in May, which is inconsistent.

the formula should not quantify over weeks in general but over weeks in May: An inclusion restriction to the wide-scope adverbial is needed in the narrow-scope adverbial Kamp and Reyle, 1993.


We set a threshold of 1x10 −6 for all matchups.

these association lists vary in length, depending on the distributions for the words, allowing them to grow up to 40, while some ended up with only one or two words.


Furthermore, the form of the utterance conveys information about the nature of the doubt that is important for the subsequent resolution of the con ict.

a collaborative natural language system must be able to generate utterances that convey doubt naturally and e ectively.


Thus it was not appropriate to use the corpus as training data for machine learning.

the following rules are based on our examination of naturally occurring dialogues, as well as our judgments of which of these naturally occurring forms was more or less successful in the dialogue.


That is, the doubted agent can persuade the doubting agent b y convincing her that he is more expert than the old source.

this extra source information is important for resolving the doubt.


Our corpus consists of spontaneous utterances that are imperfect and thus cannot be considered a gold standard" by which to judge our realizations of expressions of doubt.

we used human subjects to perform a preliminary evaluation of our generation methodology.


Unlike the former approach, this approach extracts various types of local patterns at the same time.

post-processing, such as part of speech tagging and syntactic category identication, is necessary when we apply them to NLP applications.


We believe that words, which appear far away in the previous word history, do not have as much importance as the ones closest to the word.

we need to make a restriction in the number of language models.


By these conditions, only the matched couple becomes the core of a lcc: we constrain a matched couple to be between two or more equal couples, and eliminate the possibility that the matched couple appears at the beginning or end of a phrase.

we get a new triplet (ff i , ff j , lcc ij ), where (ff i , ff j ) is the matched couple and lcc ij is the length of the longest collocation couple.


We use a stoplist to automatically identify any pair of words where a non-relevant word appears and exclude it, on the grounds that they are not very useful words for clustering.

when the program comes across a matched pair of different words in a context and if that matched pair contains a word from the stoplist, then the pair is rejected.


If y is not a complete word, either of its left or right entropy, or both must be low.

for example, t¦µu is not a word but a substring of word t¦µu (appear) the choices of the right adjacent alphabets to t¦µu must be few and the right entropy of t¦µu, when the right adjacent alphabet is tu, must be low.


In contrast, for object-drop verbs, the thematic role of the subject of the intransitive is identical to that of the subject of the transitive, not the object of the transitive.

we expect that it will be less common for the same noun to occur in subject and object position of the same object-drop verb.


Attaining a high accuracy using fully stochastic methods is particularly difficult for Japanese due to the prevalence of orthographic variants (a word can be spelled in many different ways by combining different character sets), which exacerbates the sparse data problem.

the performance of stochastic models is usually not as good as the heuristics-based language models.


If the verb in the core relation (noun -verb) consists of such a verb -noun relation, the modified noun is also assumed to carry important information, even if it does not fill the mandatory case (though the case is not analyzed in the current algorithm).

the verb -noun relation is attached to the core.


in a metonymy of the form A R V, an implicit term B will have a much tighter relationship with the explicit term A than with the predicate V. Consequently, a noun B such that f (A, Q, B) 0 ∧ f (B, R, V ) = 0 may be appropriate as an interpretation of the metonymy.

a non-zero probability should be assigned to Pr Equation 7is the probability that noun B occurs as a member of class C. This is reduced to The frequency of class C is obtained similarly: where B is a noun which belongs to the class C. Finally we derive In summary, we use the measure M as defined in Equation 3, and calculated by applying Equation (4) to Equation (9), to rank nouns according to their appropriateness as possible interpretations of a metonymy.  )


Most words can nd a unique translation candidate in a target tree, but there are cases such that there are many translation candidates in a target parsed tree for a source word.

the main task of nding word correspondences is to determine the most plausible translation word among candidates.


As a translation word dictionary between Japanese and English, we rst used J-to-E translation dictionary which has more than 100,000 entries, but we found that there are some word correspondences not covered in this dictionary.

we merged entries from E-to-J translation dictionary in order to get much broad coverage.


This accuracy level is not promising and it is not useful for later processes since it needs human checking and correction.

we subcategorize each phrasal correspondences, and check the accuracy for each subcategory.


We assume that the logarithm of F0 has a normal distribution.

we can normalize for speaker-dependent differences in pitch range by using z-scores, and we can use standard statistical analysis methods such as ANOVA.


All in all, antecedent properties can only account for a very small percentage of the variation in these prosodic cues.

we should not expect the prosodic cues to be stable, robust indicators for predicting antecedent properties in spoken dialog systems.


Collecting this amount of data in a dedicated corpus is inefficient.

further acoustic investigations do not make much sense at this point; rather, the data should be examined carefully for tendencies which can form the basis for dedicated production and perception experiments which are explicitly designed for uncovering inter-speaker variation.


When computing the probability of a combination of constituents, multiplying the separate probabilities of the constituents biases towards a low n umber of constituents.

we compute the probability of a set of constituents using a normalized version, the geometric mean 4 , rather than its product.


We divided the subjects in eight groups such that no subject had to perform more than one task on the same text and we distributed the di erent tasks evenly for each group.

we can make u n biased comparisons across texts and tasks.


But the words with multiple boundary tags constitute only about 10% of the total words predicted.

the multiple-output mode shows a good trade-off between precision and redundancy.


A match may also be found using ancestors of the concept found in the lexicon entry.

for the lexicon entry "Bishop", in English, the attached concept is "RELIGIOUS-ROLE", which is a kind of "SOCIAL-ROLE".


Although alien brains may be more or less powerful than ours (Norris 1999), it is reasonable to assume that all intelligent problem solvers are subject to the same ultimate constraints of computational power and storage and their symbol systems will reflect this.

language must use small sets of rules to generate a vast world of implications and consequences.


Regardless of what the metrics tell us as to overall quality, it still comes down to a subjective user experience.

when the product is getting close to its final shippable state, we break away from the metrics to gain insight into how users experience the grammar checker quality and usefulness.


In these cases, underspecication should be used to defer the resolution of choices.

the described module builds a lexically and scopally underspeci ed representation.


In the case of assignment of multiple sets of feature values the user is asked to disambiguate them.

every SL clause element is now tagged with certain values of diagnostic and restricting tags.


In our believe there is no need for a complete syntactic analysis to detect and correct most modi cation repairs.

in what follows, we will concentrate on this kind of repair.


It is a fundamental feature of this account that the inherited information is only default information which can be overridden.

it is not required that metaphoneme correspondences are complete and we may choose to use a metaphoneme even if one of the languages uses a different vowel in some words.


The document DTD is not used to check the validity of the input document.

input documents need not be valid XML, but must still be well-formed to be accepted by the parser.


A special notation @variable name is used to tell the transformer to continue processing with the current rule set.

if the current rule set is set2, the expression @$a indicates that processing should continue on the elements in $a using the rule set set2.


Di erent rule sets can be accessed as in the following rule le segment: X $a* -Y @set2$a "Text" -"txeT" @ set2 "Text" -"Nothing" Initially, set1 is invoked to process the element X , but then the rule set set2 is invoked to process its children.

for the input X Text X , the output is Y Nothing Y .


Grammatical relationships (GRs) form an important level of natural language processing, but different sets of GRs are useful for different purposes.

one may often only have time to obtain a small training corpus with the desired GR annotations.


With our new approach, we do not have to take into account the number of transducers R i and their auxiliary labels as was the case with the approach used before.

new transducers R i such as phonological rules can be easily inserted in the chain.


On the other hand for the non-hapax pairs the DIST condition was successful in more than 85% of the cases.

we decided that the additional DIST filtering condition be preserved for non-hapax competitors only.


For the normal node, the Chinese text is traversed in depth-first left to right order, but for an inverted node (indicated by a horizontal line in the parsing tree or indicated by a <> notation in bracketing expression), the right sub-tree is traversed before the left.

the Chinese parsing tree corresponding to Figure 3(a) is shown in Figure 3  are listed in Table 2.


In this dictionary, English translations are less ambiguous.

we translated Chinese stories into English ones.


Using this parser she extracted all head-modifier relations from the 100 million words of the British National Corpus.

the resulting co-occurrence matrix only contained the counts of the head-modifier relations.


Although length-based approaches above mentioned are simple and can achieve good performance, they are usually trained and tested in the text with the same style.


However, as the distribution of various possible translations (for each English lexicon) found in our bilingual corpus is far more diversified 4 compared with those transfer-lexicons obtained from the dictionary, only a small number of transfer-lexicons can be matched if the exact-match is specified.

each Chinese-Lexicon obtained from the dictionary is first augmented with its associated Chinese characters, and then the augmented transfer-lexicons set are matched with the target Chinese sentence(s).


The main reason is that the meaning of sentence (E1) is similar to that of (E2) but stated in different words, and the translator has merged the redundant information in his/her translation.

the length-feature prefers to delete the first source sentence.


Using chunk as the alignment structure, we can get around the problems such as PP attachment, structure mismatching across languages.


This integration strategy has been proven to be effective for base NP identification (Xun, Huang & Zhou, 2001).

our model works in three successive steps: (1) word alignment between SL and TL sentences; (2) source language chunking; (3) target language chunking.


The parser NLPWin 3 was used to tag the training corpus.


We found that many errors result from the occurrence of abbreviation of person, location, and organization.

different strategies are adopted to deal with abbreviations for different kinds of NEs.


When a parser GR has a weight of less than one, we proportionally discount its contribution to the precision and recall scores.

given a set of GRs with associated weights produced by the parser, i.e.


For instance, in unsupervised acquisition of lexical information (such as subcategorisation frames for verbs) from text, the usual methodology is to (partially) analyse the text, retaining only reliable hypotheses which are then filtered based on the amount of evidence for them over the corpus as a whole.

brent (1993) only creates hypotheses on the basis of instances of verb frames that are reliably and unambiguously cued by closed class items (such as pronouns) so there can be no other attachment possibilities.


In diachronic phonology, phonological processes that lead to insertion or deletion of segments usually operate on individual words rather than on particular sounds across the language.

i model insertion and deletion by employing a constant indel penalty for unlinked segments.


In order to indicate the difficulties of the adnoun clause analysis, we need to have some basic knowledge on the structure of Korean adnoun clause formation.

we will briefly illustrate the types of Korean adnoun clauses.


Because we found that the kernel of SVM does not strongly affect the performance of our problem through many experiments, we concluded that our problem is linearly separable.


In addition, by using kernel functions, they can learn non-linear separating boundary and dependencies between the features.

sVMs have been recently used for the natural language processing such as text categorization (Joachims, 1998;Taira and Haruno, 1999), chunk identification (Kudo and Matsumoto, 2000b), dependency structure analysis (Kudo and Matsumoto, 2000a).


The method proposed in this paper does not require aligned parallel corpora which do not exist too many at present.

without limiting applicable domains, word sequence correspondences can been extracted.


If the training samples can be separated linearly, there could exist two or more pairs of w and b that Figure 1: A separating hyperplain satisfy equation 1.

give the following constraints : (3) Figure 1 shows that the hyperplain which separates the samples.


by differentiating with respect to w and b, the following relations are obtained, and substituting equations (5) (6) into equation 4to obtain the optimization problem is transformed to maximizing the object function D subject to n i=1 λ i y i = 0 and λ i ≥ 0.


At this time, the maximal margin problem is enhanced as minimizing || w|| 2 /2 + C n i=1 ξ i , where C expresses the weight of errors.

the problem is to maximize the object function D subject to n i=1 λ i y i = 0 and 0 ≤ λ i ≤ C. For the training samples which cannot be separated linearly, they might be separated linearly in higher dimension by mapping them using a nonlinear function: where K( x, x ) is called kernel function.


As a result, the problem is to maximize the object function D subject to n i=1 λ i y i = 0 and 0 ≤ λ i ≤ C. For the training samples which cannot be separated linearly, they might be separated linearly in higher dimension by mapping them using a nonlinear function: where K( x, x ) is called kernel function.

the object function is rewritten to and the optimal hyperplain is rewritten to Note that φ does not appear in equation 11) (12.


The translation model is learned from them by SVMs described in section 2.

the optimal parameters λ * for SVMs are obtained.


The precision rate rose almost linearly according to the number of the pairs, and reaching the level-off in the recall rate since 30,000.

it suggests that the precision rate can be improved without lowering the recall rate too much by increasing the number of the bilingual word pairs in the translation dictionary.


So, in the test samples, the importance of features (2a) (2b) are increased on the coverage of the samples relatively.

if features (2a) (2b) were removed, it causes the recall rate to fall because of the low coverage of the samples.


The resulting IB set was then used for automatic detection of "suspect spots" in the sample of next 500 sentences from the corpus, and for hand-elimination of errors in this sample where appropriate (obviously, not all IB violations were genuine errors !).

we arrived at a cleaned sample of 580 sentences, which we used just in the same way for generating CB set, prun-ing it, generating IB set and pruning this set, arriving at an IB set which we used for detection of errors in the whole body of the corpus (about 20.500 sentences, 350.000 positions).


The "impossible bigrams" are a powerful tool for checking the correctness of a corpus, however, a tool which works on a very local scale only, since it is able to detect solely errors which are detectable as deviations from the set of possible pairs of adjacently standing tags.

obviously, quite a number of errors remain undetected by such a strategy.


The central observation lies then in the fact that the property of being an impossible configuration can often be retained also after the components of the "impossible bigram" get separated by material occurring inbetween them.

for example, in both our examples the property of being an impossible configuration is conserved if an adverb is placed inbetween, creating thus an "impossible trigram".


Finally, metonymic extension to the expression 'table' seems to be more neutral with respect to the number of persons sharing it (see the variants in (6)).

the syntactic subject and the verb would not agree in number in English, when the default situation concerning these dishes is present.


The analysis method that will be presented here is logically equivalent to the parsing of a dependency grammar with no locality constraints.

the complexity of a fully recursive parsing method grows more than exponentially with the length of the input.


In the Chinese aspect system, the combination of individual aspects is much more semantically restricted and much less grammatically regular than that in English.

it gives rise to more difficulties in both theoretical explanation and practical implementation.


A unique event initial time cannot be found.

the two types of aspect are incompatible and cannot combine.


First, the initial corpus was preprocessed in order to characterize texts by their topically significant words.

we retained only the lemmatized form of plain words, that is, nouns, verbs and adjectives.


It also gets better results than a system such as TextTiling that is based on word recurrence and as TOPICOLL, works with a local context.

table 1 confirms the fact reported in (Jobbins and Evett, 1998) that using collocations together with word recurrence is an interesting approach for text segmentation.


This causes the first member of the topologies associated with the S-nodes to receive a coreference tag (indicated by boxed numbers): If, as in the example of Figure 1, know's complement is indeed declarative, the foot of the complement segment can successfully unify with the root of the hate frame.

the F1 slot of the complement clause is the same object as the F1 slot of the main clause, and any fillers will seem to have moved up one level in the clause hierarchy: Filling a slot also involves coreference tags.


In (3), on the other hand, the scope of the interrogative pronoun does not include the main clause ("I know for which person x it is the case that John saw x").

the complement is interrogative and cannot share its F1 slot with that of the main clause.


Only by combining multiple inputs together often cannot reach a full understanding.

mIND applies rich contexts (e.g., conversation context and domain context) to enhance multimodal interpretation.


Intention in Figure 4(a) indicates the user is requesting RIA (Act: Request) to present her some data (Motivator: DataPresentation) about attributes of 1 The generated display has multiple layers, where the house icons are on top of the Irvington town map.

this deictic gesture could either refer to the town of Irvington or houses.


Furthermore, users can also search for objects using different constraints on data properties.

mIND models two major types of constraints: reference constraints and data constraints.


They could be entities that have been described earlier in the conversation, entities that are visible on the display, or entities that have never been mentioned or seen but exist in a database.

fine-grained constraints allow MIND to determine where and how to find the information of interest.


Based on the domain knowledge that the city object cannot have the price feature, A3 is filtered out.

both A1 and A2 are potential interpretation.


As a result, both A1 and A2 are potential interpretation.

the Content in those structures are combined using a disjunctive relation as in Figure 10(b).


In this case, based on the most recent conversation segment (DS1) in Figure 9 (also as in Figure 11b), MIND is able to derive Motivator and Method features from DS1 to update the conversation unit for U3 (Figure 11c).

this revised conversation unit provides the overall meaning that the user is interested in finding out the price information about another house MLS7689432.


For the second reference of "previous house" (A2 in Figure 12a), based on the information captured in the temporal constraint, MIND searches the conversation history and finds the most recent house explored (MLS7689432).

mIND is able to reach an overall understanding of U5 that the user is interested in comparing three houses (as in Figure 12c).


When we add the PART OF SPEECH feature, the error rate also decreases to 39.2%.

the PART OF SPEECH also helps reduce the error rate as well.


From the point of view of assigning a domain specific action to a group, this grouping is the best because each verb can be mapped to an action.

the programmer can link an action to each group rather than to each individual frame.


The only way to determine this is to consider the semantic relationship between these candidates and the verb "kill" (parse trees simplified, and only portions shown here): Although the PREDs of both S1 and S2 match that of the question "killed", only S1 matches "Lee Harvey Oswald" as the head of the logical OBJect.

for S1, the matcher awards additional credit to node [2] (Jack Ruby) for being the logical SUBJect of the killing (using anaphora resolution).


In S2, the parse tree correctly records that node [13] ("John F. Kennedy") is not the object of the killing.

despite its being closer to "killed", the candidate in S2 receives no extra credit from semantic relation matching.


Moreover, it is to be expected that the adjective in the N + Adj compound could not have modifiers.

a noun + [adjective + PP] construction, such as Fr.


The total frequency of all the documents is denoted as f req (T, D).

we sometimes use f req(  The view of the co-occurrence matrix can be further extended by assigning probabilities to each cell.


In contrast, a more complex interpretation which better matches the original argument may yield a shorter message overall.

in finding the interpretation that yields the shortest message for an NL argument, we will have produced a plausible interpretation, which hopefully is the intended interpretation.


Now, in our context, Arg contains the argument, and SysInt an interpretation generated by our system.

we are looking for the SysInt which yields the shortest message length for ML SysIntT he first part of the message describes the interpretation, and the second part describes how to reconstruct the argument from the interpretation.


In the case of a BN, the propositions are represented as nodes, and the relations between propositions as arcs.

the message length for SysInt in the context of a BN is 3.3 Calculating ML Arg differs from the system's interpretation in two respects: (1) belief, and (2) argument structure.


Since a node is inserted when no proposition in the domain matches a statement in the argument, we use an insertion penalty equal to ¢ ¡ -the probabilitylike score of the worst acceptable word-match between a statement and a proposition (Section 4.1).

the message length for node in-sertions is Node deletions = number of deleted nodes plus their designations.


Here, by subcategorizing l , which can be assigned to either axis, into , which should be assigned to the horizontal and depth axis respectively, you can assign an axis to the variables only by assigning one aspect to each.

when two independent variables, and is forbidden, for example.


Revisions of perspective are summarized as follows.

4 of domain alteration on variable X, if the number of instances of X turns into more than one and the current perspective includes no aspect relating to X, that is, X is a uniquely instantiated variable, check , and § ¦ l X in this order, and add the first possible one to the perspective.


There is no blank to mark word boundaries in Chinese text.

identifying words is difficult, because of segmentation ambiguities and occurrences of unknown words.


As mentioned before, the Sinica corpus is a word-segmented corpus based on the Chinese word segmentation standard for information processing proposed by ROCLING (Huang et al, 1997).

it contains both known words and unknown words, which are properly segmented.


However such kind of statistic does not work well when the sample size is very limited.

we propose to use reoccurrence frequency and fan-out numbers to characterize words and their boundaries (Chien, 1999).


Therefore, the languages with prefix structure, such as English, German or French, can take the benefits of this direction, because the language model/translation model can differentiate "good" hypotheses to "bad" hypotheses around the beginning of the output sentences.

the narrowing the search space by the beam search crite-ria (pruning) would not affect the overall quality.


Yet, very few textbooks provide systematic instruction or intensive exercises to overcome these difficulties with zeros.

many Japanese language teachers rely on their intuitions when explaining zeros.


ZD is a part of the CALL program for JSL learners, Zero Checker, which supports reading comprehension and writing revision process with a focus on zeros.

zD will also serve as a pre-processing module for the models of resolving and generating zeros, created within the centering framework (e.g., Grosz et al., 1995).


The words below these classes are almost all countable, with a sprinkling of plural only (like tongs).

in the three levels of the hierarchy, two are mainly uncountable, and below that mainly countable.


As can be seen in the case of tableware given above, there were classes where the single-word and multi-word expressions in the same semantic class had different countabilities.

even though there were fewer training examples, learning the NCPs differently for single and multi-word expressions and then combing the results gave an improved score: 72.0%.